Modern, Scalable Batch MVDA and MSPC

The complexity of batch manufacturing processes is increasing the need for analytics methods to solve problems such as deviation investigations, fault detection, process change assessment, and process improvements. The nature of data relationships is not only multi-variate but also often non-linear.

Legacy MSPC (Multivariate Statistical Process Control) workbenches and tools used for modelling and monitoring are not able to serve these needs anymore.

The high cost and complexity of deployment of a few purpose-built multi-variate batch modelling and monitoring applications keeps them out of reach for many and makes scaling impossible.

These applications require too much effort on contextual data preparation, are difficult to transition from off-line to online, are hard to scale and do not offer predictive modelling – making you spend more time making the application work rather than actual modelling and deployment.

There is a better way.

The most innovative and complete batch analytics, monitoring and optimization software

Being adopted by leaders in life sciences, chemicals, agrosciences, and advanced materials manufacturing

Benefits

  • Unified workflow for use by multiple personas – R&D, Process Development (PD), Manufacturing Technology (MTech, MSAT), operations, and quality ensures consistency of knowledge, understanding and investigations.
  • Data engineering and data preparation efforts are eliminated – increasing productivity of teams.
  • Elastic scaling in the cloud and on-prem ensures value-based scaling and spend.
  • Investigation and fault detection time is reduced.

Differentiators

  • Legacy MSPC (PCA, UMAP, PLS) and modern machine learning in one workflow with state-of-the-art web UX
  • Contextualized data – product, batch, process train, unit operation - ready for analytics
  • Off-line analytics and On-line monitoring in a common workflow
  • Global and local parameter significance/contribution for historical, real-time, and predicted trajectory
  • Native ingestion, assaying, and modelling of PAT spectral data for consumption in MSPC workflow

Additional Content

The Myth of the Elusive Golden Batch?

In this blog, we discuss how the challenges associated with replicating a ‘golden batch’ are often rooted in not having a manufacturing system that can recreate the exact conditions that produced the ‘golden batch’. Read on to learn how a machine learning batch analytics platform addresses these challenges.


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To achieve Agility in manufacturing – embrace Variability

With supply chain disruptions and the increasing pace of new product introductions, variability has almost become a constant for process manufacturers. In this blog series, Rajiv Anand, founder, and CEO of Quartic.ai discusses how to embrace variability to build an agile manufacturing enterprise.


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Webinar: Using Modern MVDA and Predictive Modeling to Optimize Batch Performance

Join batch manufacturing expert Dennis Brandl to understand the existing challenges and the opportunities offered by digitalization; and learn how Quartic’s Batch Analytics application uses machine-learning-enabled MVDA to identify the root causes of variability and deploy resulting predictive models, to continually optimize your batch performance.


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